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dc.creatorMohan, Charanrajes
dc.creatorCamuñas Mesa, Luis Alejandroes
dc.creatorRosa, José M. de laes
dc.creatorVianello, Elisaes
dc.creatorSerrano Gotarredona, María Teresaes
dc.creatorLinares Barranco, Bernabées
dc.date.accessioned2021-04-20T11:35:33Z
dc.date.available2021-04-20T11:35:33Z
dc.date.issued2021-03
dc.identifier.citationMohan, C., Camuñas Mesa, L.A., Rosa, J.M.d.l., Vianello, E., Serrano Gotarredona, M.T. y Linares Barranco, B. (2021). Neuromorphic Low-Power Inference on Memristive Crossbars With On-Chip Offset Calibration. IEEE Access, 9, 38043-38061.
dc.identifier.issn2169-3536es
dc.identifier.urihttps://hdl.handle.net/11441/107456
dc.description.abstractMonolithic integration of silicon with nano-sized Redox-based resistive Random-Access Memory (ReRAM) devices opened the door to the creation of dense synaptic connections for bio-inspired neuromorphic circuits. One drawback of OxRAM based neuromorphic systems is the relatively low ON resistance of OxRAM synapses (in the range of just a few kilo-ohms). This requires relatively large currents (many micro amperes per synapse), and therefore imposes strong driving capability demands on peripheral circuitry, limiting scalability and low power operation. After learning, however, a read inference can be made low-power by applying very small amplitude read pulses, which require much smaller driving currents per synapse. Here we propose and experimentally demonstrate a technique to reduce the amplitude of read inference pulses in monolithic neuromorphic CMOS OxRAM-synaptic crossbar systems. Unfortunately, applying tiny read pulses is non-trivial due to the presence of random DC offset voltages. To overcome this, we propose nely calibrating DC offset voltages using a bulk-based three-stage on-chip calibration technique. In this work, we demonstrate spiking pattern recognition using STDP learning on a small 4 4 proof-of-concept memristive crossbar, where on-chip offset calibration is implemented and inference pulse amplitude could be made as small as 2mV. A chip with pre-synaptic calibrated input neuron drivers and a 4 4 1T1R synapse crossbarwas designed and fabricated in the CEA-LETI MAD200 technology, which uses monolithic integration of OxRAMs above ST130nm CMOS. Custom-made PCBs hosting the post-synaptic circuits and control FPGAs were used to test the chip in different experiments, including synapse characterization, template matching, and pattern recognition using STDP learning, and to demonstrate the use of on-chip offset-calibrated low-power ampli ers. According to our experiments, the minimum possible inference pulse amplitude is limited by offset voltage drifts and noise. We conclude the paper with some suggestions for future work in this direction.es
dc.description.sponsorshipInternational Consortium of Nanotechnologies (ICON) Grant G0086es
dc.description.sponsorshipEuropean Union H2020 grant 824164 (HERMES)es
dc.description.sponsorshipEuropean Union H2020 grant 871371 (MeM-Scales)es
dc.description.sponsorshipEuropean Union H2020 grant 871501 (NeurONN)es
dc.description.sponsorshipEuropean Union H2020 grant 899559 (SpinAge)es
dc.description.sponsorshipEuropean Union H2020 grant PCI2019-111826-2 (APPROVIS3D)es
dc.description.sponsorshipSpanish Ministry of Science and Innovation Grant PID2019-105556GB-C31 (NANOMIND)es
dc.description.sponsorshipSpanish Ministry of Science and Innovation / FEDER Grant PID2019-103876RB-I00 (CORDION)es
dc.description.sponsorshipJunta de Andalucía (Spain) Grant US-1260118 (Neuro-Radio)es
dc.description.sponsorshipUniversidad de Sevilla ( Spain) VI PPITes
dc.formatapplication/pdfes
dc.format.extent20 p.es
dc.language.isoenges
dc.publisherIEEEes
dc.relation.ispartofIEEE Access, 9, 38043-38061.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectNeuromorphices
dc.subjectLow-power inferencees
dc.subjectPattern recognitiones
dc.subjectTemplate matchinges
dc.subjectOffset calibrationes
dc.subjectMemristive crossbares
dc.subjectNano-synapsees
dc.titleNeuromorphic Low-Power Inference on Memristive Crossbars With On-Chip Offset Calibrationes
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadoreses
dc.relation.projectID(NeurONN) H2020-871501es
dc.relation.projectIDUS-1260118es
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9367145es
dc.identifier.doi10.1109/ACCESS.2021.3063437es
dc.contributor.groupUniversidad de Sevilla. TIC178: Diseño y Test de Circuitos Integrados de Señal Mixtaes
dc.journaltitleIEEE Accesses
dc.publication.volumen9es
dc.publication.initialPage38043es
dc.publication.endPage38061es

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